/*============================================================================

libsvm

Copyright (c) 2000-2019 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

============================================================================*/

#include <stdio.h>
#include <ctype.h>
#include <stdlib.h>
#include <string.h>
#include <errno.h>
#include "svm.h"

int print_null(const char */*s*/,...) {return 0;}

static int (*info)(const char *fmt,...) = &printf;

struct svm_node *x;
int max_nr_attr = 64;

struct svm_model* model;
int predict_probability=0;

static char *line = NULL;
static int max_line_len;

static char* readline(FILE *input)
{
    int len;

    if(fgets(line,max_line_len,input) == NULL)
        return NULL;

    while(strrchr(line,'\n') == NULL)
    {
        max_line_len *= 2;
        line = (char *) realloc(line,max_line_len);
        len = (int) strlen(line);
        if(fgets(line+len,max_line_len-len,input) == NULL)
            break;
    }
    return line;
}

void exit_input_error(int line_num)
{
    fprintf(stderr,"Wrong input format at line %d\n", line_num);
    exit(1);
}

void predict(FILE *input, FILE *output)
{
    int correct = 0;
    int total = 0;
    double error = 0;
    double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;

    int svm_type=svm_get_svm_type(model);
    int nr_class=svm_get_nr_class(model);
    double *prob_estimates=NULL;
    int j;

    if(predict_probability)
    {
        if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
            info("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=%g\n",svm_get_svr_probability(model));
        else
        {
            int *labels=(int *) malloc(nr_class*sizeof(int));
            svm_get_labels(model,labels);
            prob_estimates = (double *) malloc(nr_class*sizeof(double));
            fprintf(output,"labels");
            for(j=0;j<nr_class;j++)
                fprintf(output," %d",labels[j]);
            fprintf(output,"\n");
            free(labels);
        }
    }

    max_line_len = 1024;
    line = (char *)malloc(max_line_len*sizeof(char));
    while(readline(input) != NULL)
    {
        int i = 0;
        double target_label, predict_label;
        char *idx, *val, *label, *endptr;
        int inst_max_index = -1; // strtol gives 0 if wrong format, and precomputed kernel has <index> start from 0

        label = strtok(line," \t\n");
        if(label == NULL) // empty line
            exit_input_error(total+1);

        target_label = strtod(label,&endptr);
        if(endptr == label || *endptr != '\0')
            exit_input_error(total+1);

        while(1)
        {
            if(i>=max_nr_attr-1) // need one more for index = -1
            {
                max_nr_attr *= 2;
                x = (struct svm_node *) realloc(x,max_nr_attr*sizeof(struct svm_node));
            }

            idx = strtok(NULL,":");
            val = strtok(NULL," \t");

            if(val == NULL)
                break;
            errno = 0;
            x[i].index = (int) strtol(idx,&endptr,10);
            if(endptr == idx || errno != 0 || *endptr != '\0' || x[i].index <= inst_max_index)
                exit_input_error(total+1);
            else
                inst_max_index = x[i].index;

            errno = 0;
            x[i].value = strtod(val,&endptr);
            if(endptr == val || errno != 0 || (*endptr != '\0' && !isspace(*endptr)))
                exit_input_error(total+1);

            ++i;
        }
        x[i].index = -1;

        if (predict_probability && (svm_type==C_SVC || svm_type==NU_SVC))
        {
            predict_label = svm_predict_probability(model,x,prob_estimates);
            fprintf(output,"%g",predict_label);
            for(j=0;j<nr_class;j++)
                fprintf(output," %g",prob_estimates[j]);
            fprintf(output,"\n");
        }
        else
        {
            predict_label = svm_predict(model,x);
            fprintf(output,"%g\n",predict_label);
        }

        if(predict_label == target_label)
            ++correct;
        error += (predict_label-target_label)*(predict_label-target_label);
        sump += predict_label;
        sumt += target_label;
        sumpp += predict_label*predict_label;
        sumtt += target_label*target_label;
        sumpt += predict_label*target_label;
        ++total;
    }
    if (svm_type==NU_SVR || svm_type==EPSILON_SVR)
    {
        info("Mean squared error = %g (regression)\n",error/total);
        info("Squared correlation coefficient = %g (regression)\n",
             ((total*sumpt-sump*sumt)*(total*sumpt-sump*sumt))/
             ((total*sumpp-sump*sump)*(total*sumtt-sumt*sumt))
             );
    }
    else
        info("Accuracy = %g%% (%d/%d) (classification)\n",
             (double)correct/total*100,correct,total);
    if(predict_probability)
        free(prob_estimates);
}

void exit_with_help()
{
    printf(
                "Usage: svm-predict [options] test_file model_file output_file\n"
                "options:\n"
                "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported\n"
                "-q : quiet mode (no outputs)\n"
                );
    exit(1);
}

int main(int argc, char **argv)
{
    FILE *input, *output;
    int i;
    // parse options
    for(i=1;i<argc;i++)
    {
        if(argv[i][0] != '-') break;
        ++i;
        switch(argv[i-1][1])
        {
        case 'b':
            predict_probability = atoi(argv[i]);
            break;
        case 'q':
            info = &print_null;
            i--;
            break;
        default:
            fprintf(stderr,"Unknown option: -%c\n", argv[i-1][1]);
            exit_with_help();
        }
    }

    if(i>=argc-2)
        exit_with_help();

    input = fopen(argv[i],"r");
    if(input == NULL)
    {
        fprintf(stderr,"can't open input file %s\n",argv[i]);
        exit(1);
    }

    output = fopen(argv[i+2],"w");
    if(output == NULL)
    {
        fprintf(stderr,"can't open output file %s\n",argv[i+2]);
        exit(1);
    }

    if((model=svm_load_model(argv[i+1]))==0)
    {
        fprintf(stderr,"can't open model file %s\n",argv[i+1]);
        exit(1);
    }

    x = (struct svm_node *) malloc(max_nr_attr*sizeof(struct svm_node));
    if(predict_probability)
    {
        if(svm_check_probability_model(model)==0)
        {
            fprintf(stderr,"Model does not support probabiliy estimates\n");
            exit(1);
        }
    }
    else
    {
        if(svm_check_probability_model(model)!=0)
            info("Model supports probability estimates, but disabled in prediction.\n");
    }

    predict(input,output);
    svm_free_and_destroy_model(&model);
    free(x);
    free(line);
    fclose(input);
    fclose(output);
    return 0;
}
